Tumor localization in 3D is the main goal of IGRT. Another important goal is the localization of non-cancerous objects at risk to radiation. They are usually accomplished by computing the patient's treatment-time 3D deformations based on an on-board imaging system, which is usually an x-ray based system. The treatment-time 3D deformations can be computed by doing image registration between the treatment-time reconstructed 3D image and the treatment-planning 3D image (3D/3D registration) or between the treatment-time on-board 2D projection images and the treatment-planning 3D image (2D/3D registration).
Recent advances of the IGRT registration methods emphasize real-time computation and low-dose image acquisition. Russakoff et al. [1,2], Khamene et al. [3], Munbodh et al. [4], Li et al. [5,6] rejected the time-consuming 3D/3D registration and performed 2D/3D registration by optimizing similarity functions defined in the projection domain. Other than the optimization-based methods, Chou et al. [7,8] recently introduced a faster and low-dose 2D/3D image registration by using a linear operator that approximates the deformation parameters. However, all of the above registration methods involve computationally demanding production of Digitally-Reconstructed Radiographs (DRRs) in each registration iteration (e.g., 15 ms on a modern GPU to produce a 256×256 DRR from a 256×256×256 volume [9]), which makes them difficult to be extended to support real-time (>30 fps) image registration.
Accordingly, there exists a need for real-time 2D/3D deformable registration that is fast, accurate, and robust. More specifically, there exists a need for metric-learning enabling real-time 2D/3D deformable registration.